ArticleMaterials (Basel, Switzerland)2018
Soft-Material-Based Smart Insoles for a Gait Monitoring System.
Article in Materials (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed, 27 citations in OpenAlex.
- Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.Sensors (Basel, Switzerland) · 2024Review
- Smart Shoe Insole Based on Polydimethylsiloxane Composite Capacitive Sensors.Sensors (Basel, Switzerland) · 2023Article
- Customized Textile Capacitive Insole Sensor for Center of Pressure Analysis.Sensors (Basel, Switzerland) · 2022Article
- Estimation of Knee Joint Angle Using Textile Capacitive Sensor and Artificial Neural Network Implementing with Three Shoe Types at Two Gait Speeds: A Preliminary Investigation.Sensors (Basel, Switzerland) · 2021Article
- Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring.Sensors (Basel, Switzerland) · 2021Article
- Soft Material-Enabled Electronics for Medicine, Healthcare, and Human-Machine Interfaces.Materials (Basel, Switzerland) · 2020Article
- Preliminary Clinical Application of Textile Insole Sensor for Hemiparetic Gait Pattern Analysis.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Spatiotemporal analysis of gait pattern is meaningful in diagnosing and prognosing foot and lower extremity musculoskeletal pathologies. Wearable smart sensors enable continuous real-time monitoring of gait, during daily life, without visiting clinics and the use of costly equipment. The purpose of this study was to develop a light-weight, durable, wireless, soft-material-based smart insole (SMSI) and examine its range of feasibility for real-time gait pattern analysis. A total of fifteen healthy adults (male: 10, female: 5, age 25.1 ± 2.64) were recruited for this study. Performance evaluation of the developed insole sensor was first executed by comparing the signal accuracy level between the SMSI and an F-scan. Gait data were simultaneously collected by two sensors for 3 min, on a treadmill, at a fixed speed. Each participant walked for four times, randomly, at the speed of 1.5 km/h (C1), 2.5 km/h (C2), 3.5 km/h (C3), and 4.5 km/h (C4). Step count from the two sensors resulted in 100% correlation in all four gait speed conditions (C1: 89 ± 7.4, C2: 113 ± 6.24, C3: 141 ± 9.74, and C4: 163 ± 7.38 steps). Stride-time was concurrently determined and R2 values showed a high correlation between the two sensors, in both feet (R² ≥ 0.90,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.